Detecting similar high-dimensional responses to experimental factors from human and model organism

We present a Bayesian model for analysing the effect of multiple experimental factors in two-species studies without the requirement of a priori known matching. From model studies of human diseases, conducted using *omics technologies and various model organisms, the question emerges: is there something similar in the molecular responses of the different organisms under certain conditions, such as healthy vs. diseased? Our approach provides a generative model for the task of analysing multi-species data, naturally taking into account the additional information about the affecting factors such as gender, age, treatment, or disease status.